A classification of data sets (e. g. picture data, speech signals) is the basis of an xe2x80x9cintelligentxe2x80x9d computer performance. Numerous fields of use exist, e. g. industrial production, biometrical recognition of humans, medical picture processing, etc.
The state of the art comprises a great number of classificators, e. g.
statistical classificators (Gaussian distribution classificators)
neuronic networks
synergistic algorithms
next-neighbor classificator.
A standard literature in the field of pattern recognition is Nieman, xe2x80x9cKlassifikation von Musternxe2x80x9d, Springer Verlag, 1983.
It is an object of the present invention to further improve classification quality by providing new classificators or new basic formulations of a classification.
This object is solved by one of claim 1 and 10.
Explanations are intended to contribute to a better comprehension of the technical terms used in the claims.
Identification (or Classification) of n Classes:
After providing n classes from a predetermined representative off-hand sample in a so-called learning process, an association/classification of a (still) unknown pattern into a certain class is called xe2x80x98identificationxe2x80x99. By introducing a rejection threshold, a pattern may be rejected as unknown. If it approximates a rejection class more closely than one of learned and known target classes of said identification, it is classified into said rejection class. A rejection threshold and a rejection class may be provided alternatively and cumulatively. A pattern is regarded as a xe2x80x9crejected patternxe2x80x9d (object or person), if all xe2x80x9crejectionsxe2x80x9d provided (at least one of a threshold and a class) have responded. A precondition for a successful xe2x80x9cidentificationxe2x80x9d is that the test pattern provides sufficient information for a clear association to one of said n classes of the learning process.
Verification:
An identification with n=1 is made by an a priori (previous) knowledge concerning the target class, i. e. like a binary decision, (only) an acceptance or a rejection of the test pattern (patterns used for the test, shortly: xe2x80x9ctest patternxe2x80x9d) may result.
FAR, FRR, Quality Function:
FAR (false acceptance rate) designates the rate of patterns identified false; FRR (false rejection rate) designates the rate of patterns rejected false. A quality function G=G (FAR,FRR) indicates the quality of a classification process, e. g. G=1xe2x88x92FARxe2x88x92FRR. The more precise a classification, the closer G approaches xe2x80x9conexe2x80x9d. A weighting of FAR and FRR may have an influence if one or the other parameter FAR, FRR shall be accentuated, e. g. by indicating an average value with weighting factors g1,g2, e. g. (g1xc2x7FAR+g2xc2x7FRR)/(g1+g2). In practical applications, FRR may be of more importance, so that e. g. g2=2 and g1=1 may be selected to make said quality G xe2x80x9cmeasurablexe2x80x9d and to be able to compare identifications.
The method according to the invention serves to improve classification quality.
(a) In a first step, an xe2x80x98identificationxe2x80x99 of n classes is provided, said identification being improved by a double use of a provided information. For this purpose, an information content of a test pattern is split into a necessary and a sufficient portion to be associated to a class. With said necessary portion, a preselection (pre-classification) of the classes to be considered may be effected. With said method, no precise (but rather an imprecise) classification is obtained, but the number of classes to be really considered for the pattern is substantially limited or reduced. Said step provides a xe2x80x9cbetterxe2x80x9d identification (in the meaning of the above quality function G).
(b) In a second step, classification quality is improved by an (additional) rejection class. Said class serves to support a rejection, i. e. in addition to rejections obtained for instance by threshold decisions, a particular rejection class is equally entitled with respect to the identification classes (the concrete target classes), into which a classification may be effected. With said rejection class, an a priori (previous) knowledge concerning the objects/persons (general term: patterns) to be rejected is taken into consideration insofar as e. g. a representative profile of the xe2x80x9cpatternsxe2x80x9d to be rejected is learned into said rejection class and is therefore known to the classificator.
The composition of the rejection class is xe2x80x9csubject to successxe2x80x9d, i. e. (in the meaning of the quality function) a better solution of the classification problem has to be provided by using said rejection class. When patterns to be rejected are e. g. known, they may all be learned into said rejection class. But usually, only a certain portion thereof is necessary for an improved rejection class, another portion may for example originate from a data base not relating to this problem at all.
For the selection of patterns to be rejected, selection methods may be used, e. g. testing all possibilities and using those data sets (of patterns) which yield the best result.